CMIP6 models informed summer human thermal discomfort conditions in Indian regional hotspot

The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022–2059) and far (2060–2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.

Datasets. The ERA5-HEAT (Human thermal comforT) provides the thermal index (i.e., UTCI) data which is produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) within the Copernicus Climate Change Service. The ERA5-HEAT UTCI is available globally at a high spatio-temporal resolution from 1979 to the present and can be downloaded at the Climate Data Store 48 . UTCI is estimated by considering the heat transfer between the human body and the environment 25 . Therefore, UTCI is extensively used as human biometeorology parameters to assess the relationships between outdoor environment conditions and human well-being 48 . The present study uses the hourly UTCI (°C) data at a spatial resolution of 0.25° × 0.25° from 1979 to 2010.
Methodology. The seasonal averages were obtained by averaging the daily UTCI from all sixteen CMIP6 models for each summer/hot (April through July) season during 1979-2010. ERA5 derived UTCI available at 0.25° × 0.25° spatial resolution data is considered as a reference dataset to evaluate CMIP6 models. We have then regridded all sixteen CMIP6 models onto the similar spatial grid resolution of ERA5 (0.25° × 0.25°) using the bilinear interpolation technique 67 68,69 . This is a widely used technique to test climate trends 9,70,71 . The statistical significance of the UTCI trends at each grid point for the CMIP6 MME and ERA5 datasets was determined using a two-tailed Student's t-test. Additionally, we have also computed the strong and very strong thermal stress days over NWI using a threshold of 32 °C (strong heat stress) and 38 °C (very strong heat stress) at daily summertime UTCI during the historical period (1979-2010) as defined by Di Napoli et al. 48 . Furthermore, the very strong thermal stress days were also computed during the near future (2022-2059) and far future (2060-2100) under all three projected climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) over NWI. Here, it is important to mention that, recently, various researchers started climate forecasting using AI/ML models. For example, Haq 72 used the Climate Deep Long Short-Term Memory (CDLSTM) model was developed and optimized to forecast all Himalayan states' temperature and rainfall values. Facebook's Prophet (FB-Prophet) model was also implemented to forecast and assess the performance of the developed CDLSTM model. Nevertheless, it is important to note that these aforementioned models primarily rely on data-driven approaches. In contrast, the objective of the present study is to assess dynamical (physical-based) models for forecasting the future progression of thermal stress conditions.

Results and discussion
Evaluation of thermal stress climatology. The spatial pattern of climatological (1979-2010) mean UTCI calculated from sixteen CMIP6 models and ERA5 is presented in Fig. 1a-q over the Indian subcontinent during summer. The PCC between ERA5 and sixteen individual CMIP6 models UTCI is given in Fig. 1a-p at 95% confidence level. The CMIP6 models capture the spatial variability of summertime UTCI like ERA5 over India, albeit with some differences. The PCCs between sixteen individual CMIP6 models and ERA5 are statistically significant and greater than 0.9 ( Fig. 1a-p). Hence, the MME mean is computed by averaging sixteen individual CMIP6 models. The MME is mainly used due to its better performance than individual models 73,74 .
The spatial pattern of climatological mean ERA5 and CMIP6 MME UTCI are shown in Fig. 2a,b, and the significant PCC is 0.95 between ERA5 and CMIP6 MME at a 95% confidence level. Figure 2a demonstrates that UTCI is usually greater than 32 °C over NWI compared to other Indian regions. The CMIP6 MME captures the spatial distribution of UTCI (greater than 32 °C) over NWI (Fig. 2b). The summertime ERA5 and CMIP6 MME UTCI demonstrate moderate and strong thermal stress conditions over NWI (hotspot region). The spatial pattern of bias between CMIP6 MME and ERA5 shows that CMIP6 MME usually overestimates the UTCI over NWI compared to ERA5 (Fig. 2c). The root mean square error (RMSE) varies between 0.8 to 6 °C over the entire study region, possibly due to the high overestimation of UTCI in the model (Fig. 2d).
Spatial and temporal trends. The spatial distribution of UTCI trends (ERA5 and CMIP6 MME) are shown in Fig. 3a, b during summer . The ERA5 and CMIP6 MME UTCI shows rising trends over NWI, which indicates that CMIP6 MME can capture the UTCI trend like ERA5. The UTCI trends from both are generally greater than 0.2 °C per decade. ERA5 UTCI trends are significant at a 95% confidence level over Rajasthan, Delhi, Punjab, and Himachal Pradesh, whereas CMIP6 MME shows significant rising trends over www.nature.com/scientificreports/ north India. The rising UTCI trend is also associated with rising surface temperature during summer over the NWI 29 . The temporal trends in area-averaged ERA5 and CMIP6 MME UTCI are shown in Fig. 3c,d for NWI and the entire Indian region during summer. The temporal variation in UTCI shows an increase in 1998 over NWI and the entire India, which could be due to an extreme heat wave event during 1998 75 . The UTCI increased over NWI after 2000 because the extreme heat wave events mostly happened from 2000 to 2015 in various parts of the country, including NWI 76 . The temporal trend in ERA5 and CMIP6 MME UTCI over NWI and the entire Indian region indicates the rising trends during summer Fig. 3c,d. The CMIP6 MME UTCI trend magnitude (0.21 °C per decade) is higher (~ 62%) than ERA5 for the entire Indian domain (Fig. 3c). The UTCI trend over NWI (0.19 °C per decade (ERA5) and 0.28 °C per decade (CMIP6 MME), Fig. 3d) is higher than entire India (0.08 °C per decade and 0.21 °C per decade). The results indicate that the rising thermal stress is more prominent over NWI compared to the entire Indian region. Results showed that CMIP6 MME reasonably captures thermal stress trend variability like ERA5 over NWI.
The urban centres and cities are hotter than the surrounding rural areas due to the urban heat island effect. The cities are becoming warmer day by day due to the factor that release (energy-intensity activities) and trap heat (taller concrete structures) and a lack of natural cooling influence (such as vegetation cover and water Figure 1. Spatial pattern of climatological UTCI mean of (a-p) different CMIP6 models and (q) ERA5 during summer (AMJJ, 1979-2010) over India. Pattern correlation coefficient (PCC) between individual CMIP6 models and ERA5 is given in (a-p) at 95% confidence level. The map was generated using MATLAB R2017b (www. mathw orks. com). The colormap of figures are taken in MATLAB from (https:// www. ncl. ucar. edu/ Docum ent/ Graph ics/ color_ table_ galle ry. shtml).  4 . Hence, the investigation of thermal stress is essential at the city scale. These four major metropolitan Indian cities (i.e., New Delhi, Kolkata, Mumbai, and Chennai) have been chosen because the Indian economy and infrastructure mostly depend on these cities 77 . The temporal trends in summertime CMIP6 MME UTCI are also evaluated at the four metropolitan Indian cities ( Fig. 4a-d).
The ERA5 and CMIP6 MME shows increasing UTCI trend over New Delhi, Kolkata, and Mumbai ( Fig. 4a-c), while at Chennai ERA5 (decreasing trend) and CMIP6 MME (increasing trend) (Fig. 4d). The magnitude of rising UTCI trends is maximum over New Delhi compared to other cities which demonstrates that the population in New Delhi experiences more thermal discomfort during the hot summer season. Recent study Kumar et al. 78 shows that Delhi experiences more number of heat wave events during summer than other cities which could be a possible reason for high magnitude of UTCI rising trends. The area-averaged daily UTCI from ERA5 and CMIP6 MME is used to investigate a trend in the strong thermal stress day's (UTCI > 32 °C) frequency over NWI during different year's summer (Fig. 5). ERA5 and CMIP6 MME show increasing trends (2.8 (ERA5) and 3.9 (CMIP6 MME) days per decade) in summer thermal stress days over NWI.
Future changes of human thermal comfort conditions. This section focuses on future projected change in mean UTCI from CMIP6 MME for historical and under three climate scenarios (SSP1-2.6; SSP2-4.5; SSP5-8.5) from different CMIP6 models during the summer season for the period starting from 2011 to 2100 over Indian regional hotspot-NWI. The future climate projections show increase in the summertime annual mean UTCI for three different SSPs. The significant predicted increase in UTCI is noticed 0.2 (SSP1-2.6), 0.3 (SSP2-4.5), and 0.5 (SSP5-8.5) °C per decade over NWI and all these increasing trends are statistically significant at 95% confidence level. The increase of the UTCI is 2.4 °C until the end of this century predicted by SSP1-2.6, and 3.7 °C and 6.1 °C for SSP2-4.5 and SSP5-8.5 (Fig. 6), respectively, during 1979-2100. All three SSPs indicate statistically significant (at 95% confidence level) rising trends (SSP1-2.6 (0.09 °C per decade); SSP2  Furthermore, the trends in summertime UTCI are also investigated for the near future (2022-2059) and far future (2060-2100) under all three projected climate scenarios over NWI, which is given in Fig. 6. All the three projected climate scenarios for the near future showed significant rising trends in UTCI over NWI, which are 0.18 °C per decade (SSP1-2.6), 0.36 °C per decade (SSP2-4.5), and 0.46 °C per decade (SSP5-8.5). The projected rising trends magnitude in UTCI under SSP2-4.5 and SSP5-8.5 scenarios is ~ two and threefold of the SSP1-2.6 scenario, respectively. The projected UTCI increasing trends are only significant for two climate scenarios, SSP2-4.5 (0.16 °C per decade) and SSP5-8.5 (0.67 °C per decade) for the far future period. The projected rising trends magnitude in UTCI under the SSP5-8.5 scenario for the far future is higher than the near future, and under the SSP2-4.5 scenario for the far future is less than the near future. The summer thermal stress projection has been investigated in the near (Fig. 7a-c) and the far future (Fig. 7d-f) over India. All the MME mean UTCI under the projected climate scenarios show strong thermal stress (> 35 °C) conditions in the near future over NWI, whereas moderate thermal stress conditions (~ 32 °C) over the rest of India. The thermal stress intensity over NWI gradually increases under these two projected scenarios (SSP1-2.6 and SSP2-4.5), whereas becomes maximum in the SSP5-8.5 scenario in the near future (Fig. 7a-c). The SSP1-2.6 scenario shows the strong thermal stress over NWI and across IGP region. Similarly, the thermal stress gradually increases from strong thermal stress (SSP2-4.5 scenario, Fig. 7e) to very strong thermal stress (SSP5-8.5 scenario, Fig. 7f) in the far future.There would be a significant impact over NWI with the presence of extremely high thermal stress under the SSP5-8.5 scenario. Additionally, the IGP and central Indian region will also likely experience strong thermal stress during the hot season. MME mean projected changes in summertime UTCI from CMIP6-GCMs in all three climate scenarios for the near and far future against the historical reference period (1979-2010) is presented in Fig. 8. All three www.nature.com/scientificreports/ projected climate scenarios for the near and far future is generally higher than the historical period. The changes between all the projected scenarios and historical vary less than 2 °C (for the near future, Fig. 8a-c) and between 3 and 5 °C (for the far future, Fig. 8d-f). Various researchers 79,80 have previously reported a notable increase in the evaporation of moisture and water vapour contents from the ocean, attributed to a significant rise in the sea surface temperature. Consequently, the movement of moisture-rich winds along with surface temperature rise, which gives way to hot and humid conditions that impose thermal stress on the NWI region 29 .  www.nature.com/scientificreports/ Moreover, the occurrence of very strong thermal stress days (UTCI > 38 °C) is examined using summertime ERA5 and CMIP6 MME UTCI over NWI during the historical period, as well as under various projected climate scenarios in the near and far future (Fig. 9). The ERA5 and CMIP6 MME shows 529 and 263 very strong thermal stress days over the hotspot region, respectively. CMIP6 MME has been projected to increase strong thermal stress days significantly over the twenty-first century. The number of very strong thermal stress days gradually increases under SSP1-2.6 (458 days), SSP2-4.5 (619 days), and SSP5-8.5 (925 days) scenarios in near future. There is a two-or three-fold increase in very strong thermal stress days from near future to far future (SSP1-2.6 (700 days), SSP2-4.5 (1871 days), and SSP5-8.5 (3088 days)) under all the project climate scenarios. The number of very strong thermal stress days in the far future rises three-fold of the days in the near future under the SSP2-4.5 and SSP5-8.5 scenarios. The number of very strong thermal stress days under the SSP5-8.5 scenario in the near future is about two times the historical days. An unprecedented (about 6 times the historical days) increase in very strong thermal stress days is noticed in the far future. The study's finding suggests that NWI will likely witness an unprecedented rise in the very strong thermal stress days. Saeed et al. 81 concluded, based on wet-bulb temperature, that South Asia, especially IGP, experiences deadly thermal stress at 1.5 °C of global warming. Kumar et al. 78 reported strong to very strong thermal stress over Delhi, a highly populated metro city in NWI, during summer (1990-2019). Shukla et al. 29 showed significant rising trends in UTCI (1981-2019) over NWI and strong thermal stress over the study region. Kumar and Sharma 39 demonstrate very strong heat stress conditions over a semi-arid site in NWI. Various studies have concluded the strong to very strong thermal stress conditions based on UTCI over NWI, which also aligns with the present study's findings.
The increasing occurrence of highly intense thermal stress days will pose significant risks to both human health and crop production in the region. Moreover, it will have adverse effects on the terrestrial ecosystem, contributing to forest fires and droughts 82 . The summer/hot season in tropical and sub-tropical countries brings about environmental heat conditions that have detrimental effects on multiple sectors, including human health, agriculture, the ecosystem, and the economy. Indoor thermal stress is primarily caused by local heat sources, such as glass and metal factories 83,84 . Outdoor workers, including those in agriculture, mining, quarrying, construction, and shipbuilding, face thermal stress resulting from intense sunlight and hot environmental conditions during    94 demonstrated a decline in labor productivity in India due to increasing levels of thermal stress, further impacting the economy. Therefore, the development of appropriate guidelines to address these issues is crucial for improving the health, productivity, and overall economy of indoor and outdoor workers. This study underscores the importance of implementing thermal stress management plans to mitigate adverse impacts across various sectors.

Conclusions
The present study evaluates the capability of CMIP6 to assess thermal stress condition over NWI during summer.
The findings of the present study are summarized as follows.
• The spatial pattern of the summertime climatological (1979-2010) mean of ERA5 UTCI shows the strong thermal stress over a hotspot region (i.e., NWI). The CMIP6 MME reasonably captures the strong thermal stress like ERA5 over NWI. The spatial trends in UTCI (ERA5 and CMIP6 MME) show the rising trends over  NWI. The CMIP6 MME shows more significant trends than ERA5 and trend magnitude is usually greater than 0.2 °C per decade. • The area-averaged summertime ERA5 and CMIP6 MME UTCI for the entire India and NWI depict that the UTCI rising trend magnitude is higher over NWI than entire India. It demonstrates that NWI experiences more strong thermal stress than India. The summertime UTCI trends over New Delhi are higher than in other metropolitan cities (i.e., Kolkata, Mumbai, and Chennai), which could be due to frequent and more number of summertime heat wave events over the region. • The number of summertime thermal stress days (UTCI > 32 °C) from ERA5 and CMIP6 MME shows the increasing trends over NWI. The rising trends in the thermal stress days are ~ 2.8 days per decade (ERA5) and 3.9 days per decade (CMIP6 MME) over the study regions. • The future climate projections show an increase in the mean UTCI under all three projected climate scenarios over NWI during summertime. The results showed a significant rising trend in summertime CMIP6 MME UTCI under different projected scenarios (2011 to 2100) for SSP1-2.6 (0.9 °C per decade), SSP2-4.5 (0.26 °C per decade), and SSP5-8.5 (0.56 °C per decade), respectively. • All three projected scenarios show the strong thermal stress (> 35 °C) condition in the near future over NWI, whereas moderate thermal stress condition (~ 32 °C) over rest part of India. Similarly, the thermal stress gradually increases from strong thermal stress (SSP2-4.5 scenario) to very strong thermals stress (SSP5-8.5 scenario) in the far future. SSP5-8.5 scenario in far future affects the NWI with very strong thermal stress and in the IGP and central Indian region with strong thermal stress.The changes in the UTCI from all three projected scenarios in the near and far future are always higher than in the historical period, and the differences vary between 1 to 5 °C. The number of summertime very strong heat stress days (UTCI > 38 °C) over NWI in the near future showed a two to three-fold rise in the far future under all the projected future scenarios.
Overall, the present study reveals a notable increase in the frequency of intense thermal stress days over NWI during the summer season. Consequently, it highlights the urgent need to evaluate the future implications of such severe heat stress conditions on human health in the NWI region. To mitigate the adverse effects on human well-being, the implementation of an early warning system based on thermal stress assessment becomes crucial. Therefore, it is essential to further develop and deploy AI/ML-based models that can accurately predict local-scale thermal stress conditions.
The limitation of the present study is the coarse resolution of all CMIP6 models because it can mask important local features and phenomena, such as extreme heat stress events, orographic effects, or land-sea contrasts. Moreover, climate model (CMIP6) results and interpretation, particularly in future scenarios, have inherent limitations in terms of reliability. Assessing the accuracy of data for different models by comparing them to observations in historical results provides some insight, but it is unclear whether this fully validates their accuracy in a changing future climate. Some studies have highlighted and discussed limitations in the interpretation of heat stress indicators such as UTCI 17,47 .